Estimation of homeostatic dysregulation and frailty using biomarker variability: a principal component analysis of hemodialysis patients
Bibliographic record
Abstract
Increased intraindividual variability in several biological parameters is associated with aspects of frailty and may reflect impaired physiological regulation. As frailty involves a cumulative decline in multiple physiological systems, we aimed to estimate the overall regulatory capacity by applying a principal component analysis to such variability. The variability of 20 blood-based parameters was evaluated as the log-transformed coefficient of variation (LCV) for one year's worth of data from 580 hemodialysis patients. All the LCVs were positively correlated with each other and shared common characteristics. In a principal component analysis of 19 LCVs, the first principal component (PC1) explained 27.7% of the total variance, and the PC1 score exhibited consistent correlations with diverse negative health indicators, including diabetes, hypoalbuminemia, hyponatremia, and relative hypocreatininemia. The relationship between the PC1 score and frailty was subsequently examined in a subset of the subjects. The PC1 score was associated with the prevalence of frailty and was an independent predictor for frailty (odds ratio per SD: 2.31, P = 0.01) using a multivariate logistic regression model, which showed good discrimination (c-statistic: 0.85). Therefore, the PC1 score represents principal information shared by biomarker variabilities and is a reasonable measure of homeostatic dysregulation and frailty.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".